Is your master data management “mature” enough to actually be useful?
You’d like to think it is. Heck, I’d like to think it is. After all, there’s no shortage of examples of how MDM can help improve data, reduce costs, and yield more reliable information. And you’d think companies would know how to do this by now — after all, MDM isn’t exactly new.
That’s why I cringed when I was reading a recent piece by Gartner Research Vice President Ted Friedman and saw this (the added emphasis is mine):
“Whatever an organisation’s key business transformation objectives, success will depend directly on the extent to which this master data is understood, consistent, accurate and trusted. At present, though, few organisations have master data management (MDM) processes mature enough to use this data to reduce costs and increase revenue.”
Does anybody else find that statement kind of depressing?
There are two points Friedman makes that temper this downer. First, you’ll note the “s” in organization — that’s because Friedman’s piece appeared in the UK-based Computerworld. So maybe, just maybe, he means <i>UK</i> companies don’t have mature enough processes.
We can hope, anyway.
Second — and this is where I lose hope that he’s only talking about UK firms — he says the main problem isn’t the technology, but people involvement in MDM. Specifically, he cites the lack of effective governance models and development of suitable skills and roles.
Well, to be fair, we knew that governance thing was a problem for MDM and, really, all data.
So what can be done to change this? Friedman suggests that those involved in MDM — whether you’re in IT or a business leader — focus on four specific areas where MDM can deliver value in the 2013:
Use MDM to improve service levels. This applies to both external customers and B2B partners. “MDM can help by improving the accuracy and effectiveness of service delivery processes,” Friedman writes.
This is an established use case for MDM. TDWI published a mini-case study describing how one of the world’s largest credit unions used MDM to improve the data on its half a million customers. The end results were higher level of service, faster transaction service in its 50 branches and a more consistent service experience across channels.
This is also a great place to start, because it’s easy to see the connection between MDM, data governance and better customer service. Finally, it’s an obvious starting point for talking about governance and how you can add routine steps to support it. (Personally, I like the word routine more than “processes,” which sounds like much more work.)
MDM can reduce waste, cut costs and generally improve productivity. By using MDM for customer service, you could cut costs per customer order by 20 to 40 percent by eliminating manual processes and reducing errors, according to Jason Lovinger, the vice president for the global center of excellence at SAP.
The results can lead to a shorter cycle time and decreased labor costs, and “improve net revenues four to five percent,” he continues.
MDM as a way to innovate business models and add new markets. MDM helped the Nielsen Company launch a new line of business, as well as new internal projects, according to a TechTarget podcast.
MDM can be useful in targeting the right customers in new marketing campaigns, for instance. It’s certainly tied to revenue growth in different ways. Data intelligence firm InsightSquared reports that data quality best practices — which would include MDM — boost revenue at companies by 66 percent.
MDM should support “Big Data” projects. This is a new use case for MDM, but it’ll become important as companies move from proof-of-concept with Big Data and begin to filter through their large datasets.
“There should be a symbiotic relationship between MDM and big data; big data technology can feed insights to MDM, and MDM can feed master data definitions to big data,” wrote David Corrigan, director of strategy for IBM's InfoSphere portfolio, which includes IBM’s MDM group, last April.
Although Corrigan didn’t share a specific use case in this piece, he does include examples demonstrating how Big Data and MDM can supplement one another. An example: IBM clients are using Big Data to fill in the gaps in the 360-degree view MDM promised.
The challenge will be using your existing MDM tools to support Big Data. Some experts warn that Big Data will require more of a federated approach to MDM, rather than the repository-style deployments some companies pursued.